Semi-supervised learning (SSL) is an another approach that aims at mitigating the burden of annotation. In such approaches a small set of labeled target data is used to label the remaining unlabeled data in an automated way thus leveraging the unlabeled data. SSL has shown significant progress for semantic segmentation in recent years [1]}, [2]}, [3]}, [4]}, [5]}. A potential problem with such approaches is overfitting of learned models to the small set of labeled target data, hence leading to a need to improve model generalization.
Approaches for SSL include GAN-based adversarial learning [6]}, [3]},
pseudo supervision [1]}, [9]}, and consistency regularization [2]}, [11]}, [12]}.
